Today, information Networks play an important role in supply chain management. Therefore, in this article, clustering-based routing protocols, which are one of the most important ways to reduce energy consumption in wireless sensor Networks, are used to optimize the supply chain informational cloud Network. Accordingly, first, a clustering protocol is presented using self-organizing map neural Network, SOM. Second, we cluster the Network nodes based on two criteria of neighborhood and energy level using K-means clustering pattern. Third, we survey the efficiency and inefficiency of the clusters to balance the energy properly among the clusters. Then, to increase the Network Lifetime and to maintain the Network DEA method is used. Finally, the model is tested for the information Network of oil supply chain.

The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor Networks (CSNs). One solution for this problem is that the sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the Lifetime maximization will become an important issue. In this paper, maximizing the Lifetime of a CSN is investigated by selecting both the cooperative sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NP-complete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convex-based optimization algorithms with low order of complexity. First algorithm applies the known instantaneous Signal-to-Noise-Ratio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the needed time for obtaining answers, the Bisection method is proposed for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the Network in case of Lifetime and energy consumption.

Wireless sensor Networks contain of many sensors that can serve as powerful tools for data collection in environments. A key challenge in these Networks is the limited Lifetime of sensor batteries. Ideally, all nodes would exhaust their energy simultaneously or through regular scheduling, maximizing the Lifetime. Consequently, the primary concern is achieving optimal energy utilization to extend the Network's Lifetime over a logical duration. Depleting the batteries of the sensors means stopping the operation of the Network, because it is practically impossible to replace the batteries of thousands of nodes. To address this issue, the low energy adaptive clustering hierarchical (LEACH) protocol has been widely recognized as one of the prominent solutions for clustering WSNs. However, the random selection of cluster heads in each round under the LEACH protocol fails to guarantee proper convergence. To overcome this limitation, this paper proposes a refined approach by utilizing a genetic algorithm and a novel objective function that incorporates various factors, including energy level and distance. The algorithm employs chromosomes to represent CHs and facilitates the selection of cluster nodes. Notably, the proposed algorithm dynamically performs clustering, meaning that clustering is conducted iteratively, considering identifying dead nodes. By leveraging this approach, the algorithm significantly enhances the clustering quality, ultimately leading to an increased Network Lifetime. To validate its effectiveness, it is compared with LEACH, LEACH_E and LEACH_EX algorithms, demonstrating its superior capabilities. On average, the proposed algorithm has more alive nodes in the Network, and the remaining energy is at least 11% higher than the best other algorithms.

Abstract: Wireless Sensor Networks (WSNs) comprising of tiny, power-constrained nodes are getting very popular due to their potential uses in wide applications like monitoring of environmental conditions, various military and civilian applications. The critical issue in the node is energy consumption since it is operated using battery, therefore its Lifetime should be maximized for effective utilization in various applications. In this paper, a Game theory based Hybrid MAC protocol (GH-MAC) is proposed to reduce the energy consumption of the nodes. GH-MAC is combined with the game based energy efficient TDMA (G-ETDMA) for intra-cluster communication between the cluster members to head nodes and Game theory based nanoMAC (G-nanoMAC) protocol used for inter-cluster communication between head nodes. Performance of GH-MAC protocol is evaluated in terms of energy consumption, delay and compared with conventional MAC schemes. The results obtained using GH-MAC protocol shows that the energy consumption is enormously reduced and thereby the Lifetime of the sensor Network is enhanced.

The area of wireless sensor Network is shrouded with significant ranges of problems where majority of the prior research attempts were carried out to address energy conservation issues. The present manuscript introduces a novel technique called as ENLPL or Enhancing Network Lifetime Using Probabilistic Logic where the prime focus is laid on conserving the energy for large scale wireless sensor Network. ENLPL presents design of inter and intra clustering approach using probability theory. It also laid emphasis on ensuring maximum participation of the sensor nodes in selection process of cluster-leader with an aid of maximum number of parameters. The outcome of the study was compared with conventional and standard hierarchical clustering algorithm (LEACH) to find ENLPL ensures better energy efficiency for large number of the sensor. With the adoption of the probabilistic reasoning technique, the simulation results shows that the proposed system maximizes the cumulative Lifetime of the Network, along with an efficient selection process of the cluster leader in every iteration.

Todays, Wireless Body Sensor Networks (WBSNs) are used as a useful way in health monitoring. One of the most important problems regarding Wireless Body Sensor Network (WBSNs) is Network Lifetime. This factor mainly relies on the energy consumption of sensors. In fact, during capturing vital sign data and also communicating them to the coordinator, the biosensors consume energy. In this article, we are interested to propose an energy efficient Adaptive Sampling (AS) rate specification algorithm to set the amount of sensed data. According to the National Early Warning Score (NEWS), the sensors gather data and detect emergency data. Two scenarios have been used; the first is utilizing context recognition to indicate the active and sleep sensors in different time slices and the second is using watchdog sensors for checking patient situation in critical condition. Simulation results show that the proposed method can save energy and increase Network Lifetime by up to 4 times more than the previous work. In addition, our methods allow on average 75% improvement in overhead data reduction while maintaining more than 90% data integrity.